Image mining using directional spatial constraints

Abstract

Spatial information plays a fundamental role in building high-level content models for supporting analysts' interpretations and automating geospatial intelligence. We describe a framework for modeling directional spatial relationships among objects and using this information for contextual classification and retrieval. The proposed model first identifies image areas that have a high degree of satisfaction of a spatial relation with respect to several reference objects. Then, this information is incorporated into the Bayesian decision rule as spatial priors for contextual classification. The model also supports dynamic queries by using directional relationships as spatial constraints to enable object detection based on the properties of individual objects as well as their spatial relationships to other objects. Comparative experiments using high-resolution satellite imagery illustrate the flexibility and effectiveness of the proposed framework in image mining with significant improvements in both classification and retrieval performance.